Online or onsite, instructor-led live Mathematica training courses in Vienna.
NobleProg Wien
Modecenterstraße 22, Wien, Austria, 1030
Overview
Our training facilities are located at Modecenterstrasse 22 in Vienna. Our spaci...
Overview
Our training facilities are located at Modecenterstrasse 22 in Vienna. Our spacious training rooms offer optimal training conditions for your needs.
For questions and training registrations, please contact our company headquarters:
NobleProg Europe GmbH,
Kolonnenstr. 8
10827 Berlin
Deutschland
Directions
The NobleProg training facilities are conveniently located near the A23 motorway and the nearest subway station, within walking distance, is the Zippererstrasse station.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants within walking distance.
Introduces the core technologies needed to become an adept user of Mathematica, including the Wolfram Language, the notebook interface, programming fundamentals, visualization and interactivity features, and mathematics and statistics functionality. Small class sizes allow for interaction with the instructor, and in-person courses allow additional time for discussion, exercises, and Q&A.
The "Mathematica - wprowadzenie, wizualizacja i prezentacja danych" training is aimed at participants with basic mathematical knowledge. Mathematica, as a powerful mathematical engine, enables effective calculations, creating text documents, building charts and presenting data. Participants will gain practical skills in working with Mathematica, used, among others, in mathematics, physics, biology, financial analysis and other fields.
This instructor-led, live training in Vienna (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis.
By the end of this training, participants will be able to:
Build and train machine learning models.
Import and prepare data for machine learning.
Separate training data from test data.
Explore deep learning and neural network applications in data analysis.
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